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The model uses a ResNet-18 VD backbone for feature extraction, followed by transformer encoder-decoder layers that directly predict bounding boxes and class labels without anchor boxes or NMS post-processing, enabling end-to-end differentiable detection with reduced inference latency.","intents":["detect and localize objects in images with real-time performance constraints","integrate object detection into production systems requiring sub-100ms inference","leverage transformer attention for improved small-object and crowded-scene detection","deploy detection models on edge devices or cloud endpoints with minimal overhead"],"best_for":["computer vision engineers building real-time detection pipelines","teams deploying object detection on resource-constrained hardware (mobile, edge)","researchers comparing transformer vs CNN-based detection architectures","production systems requiring COCO/Objects365 dataset compatibility"],"limitations":["ResNet-18 VD backbone limits feature richness compared to ResNet-50/101 variants; trades accuracy for speed","Transformer decoder adds computational overhead during inference; not optimal for extremely latency-critical applications (<50ms)","No built-in support for video frame batching or temporal consistency across frames","Requires careful input normalization (ImageNet stats); sensitive to image preprocessing variations"],"requires":["Python 3.8+","PyTorch 1.9+ or ONNX Runtime for inference","transformers library 4.25+","CUDA 11.0+ for GPU acceleration (optional but recommended)","Input images in standard formats (JPEG, PNG, BMP)"],"input_types":["image (single or batch)","image tensor (B, 3, H, W format)","image file path or URL"],"output_types":["bounding boxes (x1, y1, x2, y2 or cx, cy, w, h format)","class labels (integer indices)","confidence scores (0-1 float)","structured detection results (JSON or dict)"],"categories":["image-visual","computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_r18vd_coco_o365__cap_1","uri":"capability://image.visual.multi.dataset.transfer.learning.with.coco.and.objects365.pre.training","name":"multi-dataset transfer learning with coco and objects365 pre-training","description":"Model is pre-trained on both COCO (80 classes, ~118K images) and Objects365 (365 classes, ~600K images) datasets, enabling transfer learning across diverse object categories and domain variations. 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The export process preserves the transformer architecture and attention mechanisms, maintaining accuracy while enabling optimized inference on CPUs, GPUs, and edge accelerators (TPU, NPU).","intents":["deploy detection models on mobile devices (iOS, Android) without PyTorch runtime","optimize inference on cloud platforms (AWS SageMaker, Azure ML) using ONNX Runtime","accelerate detection on specialized hardware (NVIDIA TensorRT, Qualcomm Snapdragon)","integrate detection into non-Python applications (C++, Java, JavaScript)"],"best_for":["mobile and edge device developers","cloud infrastructure teams optimizing inference costs","embedded systems engineers deploying on IoT devices","cross-platform application developers (web, desktop, mobile)"],"limitations":["ONNX export may lose some dynamic control flow; certain attention patterns may require custom operators","TorchScript export requires careful handling of Python-specific code; not all PyTorch operations are scriptable","Quantization (INT8, FP16) during export may reduce accuracy by 1-3% depending on calibration data","ONNX Runtime performance varies by hardware; GPU acceleration requires CUDA/cuDNN installation"],"requires":["Python 3.8+","PyTorch 1.9+","onnx library 1.12+","onnxruntime for inference validation","Target runtime environment (ONNX Runtime, TensorRT, CoreML, etc.)"],"input_types":["PyTorch model checkpoint","model configuration (YAML or JSON)","sample input tensor for tracing"],"output_types":["ONNX model file (.onnx)","TorchScript model file (.pt)","quantized model variants (INT8, FP16)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_r18vd_coco_o365__cap_4","uri":"capability://image.visual.confidence.based.filtering.and.nms.free.post.processing","name":"confidence-based filtering and nms-free post-processing","description":"Provides built-in confidence score filtering and optional soft-NMS (non-maximum suppression) post-processing without requiring manual NMS implementation. The model outputs raw detection scores that can be thresholded directly, and includes optional deduplication logic for overlapping boxes, eliminating the need for external NMS libraries while maintaining flexibility for custom post-processing pipelines.","intents":["filter low-confidence detections to reduce false positives in production systems","apply custom confidence thresholds per object class for domain-specific tuning","handle overlapping detections with soft-NMS for applications requiring all detections (tracking, counting)","integrate detection results directly into downstream applications without post-processing overhead"],"best_for":["production systems requiring tunable false-positive rates","multi-class detection applications with class-specific confidence requirements","tracking and counting systems that need all detections, not just top-K","real-time systems where post-processing latency is critical"],"limitations":["Default confidence threshold (0.5) may not be optimal for all domains; requires empirical tuning","Soft-NMS adds ~5-10ms per image; not suitable for extreme latency constraints (<20ms)","No built-in class-specific thresholding; requires manual per-class filtering logic","Overlapping detection handling assumes IoU-based similarity; may fail on visually similar but semantically different objects"],"requires":["Python 3.8+","PyTorch 1.9+","transformers library 4.25+","Optional: torchvision for advanced NMS variants"],"input_types":["raw model outputs (logits, bounding boxes)","confidence threshold (float 0-1)","NMS parameters (IoU threshold, soft-NMS sigma)"],"output_types":["filtered bounding boxes","filtered class labels","filtered confidence scores","detection count per class"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_r18vd_coco_o365__cap_5","uri":"capability://tool.use.integration.huggingface.hub.integration.with.model.versioning.and.auto.download","name":"huggingface hub integration with model versioning and auto-download","description":"Model is hosted on HuggingFace Hub with automatic checkpoint management, versioning, and cached downloads via the transformers library. 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The model can be packaged into Docker containers with inference servers (TorchServe, Triton, KServe) for scalable cloud deployment with automatic load balancing and GPU resource management.","intents":["deploy detection models to Azure ML endpoints for production inference","scale detection inference across multiple GPUs/TPUs in cloud environments","integrate detection into serverless inference pipelines (AWS Lambda, Google Cloud Functions)","monitor and log detection predictions in cloud-native observability platforms"],"best_for":["enterprise teams deploying ML models to cloud platforms","teams requiring auto-scaling and high-availability detection services","organizations with existing Azure/AWS infrastructure","production systems needing monitoring, logging, and audit trails"],"limitations":["Cloud deployment adds 50-200ms latency due to network round-trips; not suitable for sub-100ms SLA","Containerization overhead (Docker image size ~2-3GB) increases deployment time","GPU quota limits on cloud platforms may constrain concurrent inference requests","Cost scales with inference volume; batch processing more cost-effective than real-time endpoints"],"requires":["Python 3.8+","Docker for containerization","Azure ML SDK or AWS SageMaker SDK","Cloud account with GPU quota (optional but recommended)","Model in ONNX or SavedModel format for cloud compatibility"],"input_types":["image file or base64-encoded image","image URL","batch of images in JSON format"],"output_types":["JSON response with bounding boxes and scores","structured detection results","inference latency and resource metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ or ONNX Runtime for inference","transformers library 4.25+","CUDA 11.0+ for GPU acceleration (optional but recommended)","Input images in standard formats (JPEG, PNG, BMP)","PyTorch 1.9+","Custom dataset in COCO JSON format for fine-tuning","GPU with 8GB+ VRAM for efficient fine-tuning","PyTorch 1.9+ with dynamic shape support","GPU with sufficient VRAM for batch size (8GB+ recommended)"],"failure_modes":["ResNet-18 VD backbone limits feature richness compared to ResNet-50/101 variants; trades accuracy for speed","Transformer decoder adds computational overhead during inference; not optimal for extremely latency-critical applications (<50ms)","No built-in support for video frame batching or temporal consistency across frames","Requires careful input normalization (ImageNet stats); sensitive to image preprocessing variations","Pre-training on COCO+Objects365 may introduce class imbalance bias; rare classes underrepresented","Fine-tuning on significantly different domains (e.g., medical, satellite imagery) may require careful hyperparameter tuning to avoid catastrophic forgetting","No explicit domain adaptation mechanisms; assumes reasonable visual similarity between pre-training and target domains","Class overlap between COCO and Objects365 not explicitly handled; may cause confusion in multi-dataset inference","Dynamic resolution adds ~10-20ms per batch due to shape inference and padding computation","Memory usage varies with input dimensions; large batches of high-resolution images may exceed VRAM limits","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5947790753797414,"quality":0.24,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-04-22T08:08:25.099Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":521638,"model_likes":5}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pekingu--rtdetr_r18vd_coco_o365","compare_url":"https://unfragile.ai/compare?artifact=pekingu--rtdetr_r18vd_coco_o365"}},"signature":"mrdLDVbec5nROWZW3AVLlXBoRi46Q/1jxxaG/+6rVZKSQlsOb1DApNDHIjTeDqicw1kx64JPPziWbTop224sAg==","signedAt":"2026-06-21T21:37:27.318Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pekingu--rtdetr_r18vd_coco_o365","artifact":"https://unfragile.ai/pekingu--rtdetr_r18vd_coco_o365","verify":"https://unfragile.ai/api/v1/verify?slug=pekingu--rtdetr_r18vd_coco_o365","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}